For decades, eCommerce has been built around human behavior: browsing websites, comparing products, reading reviews, and completing purchases manually. 

But the rise of artificial intelligence is fundamentally reshaping that process. 

Increasingly, consumers are turning to AI assistants and large language models (LLMs) to help them discover, evaluate, and eventually purchase products.

In fact, 37% of consumers now begin their searches on LLMs rather than traditional search engines like Google. 

The only major friction point left is trust during the purchase process itself. Today, 58% of consumers use AI for product research, yet only 17% trust AI to complete purchases on their behalf. That gap will close over time, and when it does, brands will face a simple reality: either become readable and accessible to AI agents, or risk becoming invisible.

This is where agentic commerce enters the picture.

Agentic commerce describes a process where AI agents can independently research, compare, recommend, and even purchase products on behalf of consumers. Rather than consumers manually navigating websites, intelligent agents will increasingly handle the process autonomously.

Now, in simple terms, this has one major impact: AI does not interpret your brand, and is not impressed with flashy, clean eCommerce web experiences. It only cares about your data. If your product information is fragmented, inconsistent, or incomplete, AI agents will struggle to understand your products and may bypass your brand entirely.

The good news is that agentic commerce will not replace eCommerce overnight, if at all. Instead, it will emerge as an entirely new commerce channel, one powered by machine-to-machine interaction rather than human browsing.

The question brands need to ask now is simple: when AI becomes the buyer, will it choose you?

To answer that, brands first need to understand the infrastructure powering this shift. Agentic commerce is not driven by AI alone, it depends on the protocols and standards that allow AI agents, retailers, payment systems, and platforms to communicate with each other securely and in real time.

These emerging frameworks will shape how products are discovered, evaluated, and ultimately purchased in the age of autonomous commerce.

What are Agentic Commerce Protocols?

At the foundation of agentic commerce are the protocols that allow systems to communicate with one another in real time.

An agentic commerce protocol is a standardized framework that enables AI agents to securely discover, interact with, and transact with retailers or service providers on behalf of users. These protocols support autonomous purchasing workflows from product discovery through checkout and payment.

In simple terms, these protocols act as the “rules of engagement” between merchants and AI agents.

Without standards, every AI assistant would need custom integrations for every retailer, payment provider, inventory system, and commerce platform. That would make large-scale agentic commerce nearly impossible.

Protocols like Universal Commerce Protocol (UCP) and Agentic Commerce Protocol (ACP), which we’ll dive deeper into in a minute, aim to solve this problem by creating common frameworks for interoperability.

While both protocols are designed to support AI-driven commerce experiences, they approach the future very differently.

The Open Web Model: Universal Commerce Protocol (UCP)

Universal Commerce Protocol (UCP) follows an open-web philosophy.

Under this model, any merchant and any AI agent can participate as long as they comply with the protocol standards. Strategically, UCP is designed to preserve the openness of the web while enabling AI-native commerce experiences.

Merchants maintain ownership of their checkout experience, pricing logic, and customer interactions. They also remain discoverable through search indexing and accessible across a broad ecosystem of AI assistants and services.

The core of UCP is the manifest file. Every UCP-compliant merchant publishes a machine-readable file that describes the merchant’s capabilities. This file acts like a directory entry for AI agents, telling them how to interact with the merchant’s backend systems and which tools or APIs are available for completing specific tasks.

For example, an AI agent could use a merchant’s manifest to:

• Search a product catalog
• Check live inventory
• Validate pricing
• Initiate checkout
• Complete transactions

This is the vision for UCP; in practice today, product information is sent through Google Merchant Center to Google, making it effectively a feed-based approach, very similar to how ACP works.

That said, the protocol is evolving quickly.

With the introduction of newer capabilities such as Catalog, announced in March 2026, UCP is beginning to support more real-time, conversational interactions between AI agents and merchant systems.

This opens the door to far more advanced use cases, including:

• Querying live inventory during conversations
• Validating current pricing in real time
• Personalizing recommendations dynamically
• Supporting interactive shopping experiences directly within AI interfaces

One of UCP’s strengths is its flexibility. Merchants can begin with a relatively lightweight feed-based setup and gradually evolve toward more advanced, real-time integrations as their infrastructure matures.

It’s also important to note that momentum for UCP is building. Google has recently announced that major platforms such as Commerce Inc, Salesforce, and Stripe will implement UCP, signaling growing ecosystem support and increasing the likelihood of broader adoption across the industry.

The Platform Model: ACP (Agentic Commerce Protocol)

Agentic Commerce Protocol (ACP) is the alternative “Marketplace” standard.

Unlike UCP’s “open web” philosophy, ACP operates closer to a platform model. Merchants need to explicitly register or integrate with the specific ACP endpoints (maintained by partners like Stripe for the payment) to be accessible. It prioritizes a highly controlled, high-quality user experience over broad, open-web discoverability.

To simplify merchant participation, Stripe created the Agentic Commerce Suite, which acts as an aggregation layer for ACP product feeds. Instead of submitting separate feeds to each agent platform, merchants can send a single feed to Stripe, which then syndicates it to supported platforms such as ChatGPT, Microsoft Copilot, and future ACP-compatible environments.

This approach significantly reduces integration complexity for merchants while helping AI platforms maintain more standardized product experiences.

Several early examples of ACP-style commerce are already emerging.

Microsoft Copilot currently offers a Checkout feature that enables users to complete purchases directly within the conversational interface. Rather than redirecting users to external websites, transactions can happen seamlessly inside the AI interaction itself.

ChatGPT also announced its “Buy For Me” feature in September 2025. However, OpenAI later scaled back aspects of the in-product transaction experience, highlighting one of the key realities of agentic commerce:

AI purchasing remains more difficult than many initially expected.

Challenges around trust, liability, consumer comfort, fraud prevention, and transaction responsibility are still significant barriers. Consumers may be comfortable asking AI for recommendations, but fully delegating purchasing authority is a much bigger psychological leap.

Even so, ACP demonstrates how platform-centric ecosystems may evolve into highly curated commerce environments where AI assistants manage large portions of the customer journey directly.

Competing Standards, Uncertain Outcomes

It is still far too early to predict whether ACP or UCP will emerge as the dominant standard.

Both protocols are in the early stages of development, adoption remains limited, and implementations are still evolving rapidly.

History shows that technological standards are rarely determined solely by technical superiority. Instead, success is usually driven by adoption, ecosystem power, and distribution.

A technically elegant protocol with limited adoption has little influence. Meanwhile, a simpler standard backed by major platforms can quickly become the foundation for an entire industry.

The future of agentic commerce will likely be shaped less by protocol design and more by which ecosystems gain traction with consumers, merchants, AI providers, and payment platforms.

For brands, the key takeaway is simple: do not wait for a winner to emerge before preparing.

How to Prepare for Agentic Commerce

Regardless of which protocol ultimately succeeds, there are several steps brands can take now to prepare for the future of AI-driven commerce.

1. Invest in Structured Product Data

AI agents rely entirely on data quality.

Incomplete descriptions, inconsistent attributes, missing specifications, and poor taxonomy structures make it difficult for AI systems to understand and recommend products accurately.

Brands should focus on creating clean, enriched, and standardized product information that is machine-readable across channels.

2. Prioritize Interoperability

Future commerce ecosystems will depend on systems being able to communicate with one another seamlessly.

Brands should evaluate whether their current commerce infrastructure supports APIs, real-time data exchange, and flexible integrations that can evolve alongside emerging protocols.

3. Improve Product Context and Metadata

AI agents need more than product titles and pricing.

Rich metadata, detailed specifications, compatibility information, sustainability details, customer sentiment, and contextual attributes will increasingly influence how products are ranked and recommended by AI systems.

Consumers will still browse websites for years to come, but increasingly, AI agents will act as intermediaries between customers and brands. Discovery, evaluation, comparison, and even purchasing decisions will gradually shift toward machine-driven interactions.

Protocols like ACP and UCP are laying the groundwork for that future today.

Some ecosystems will favor openness and interoperability. Others will prioritize tightly controlled platform experiences. Both models may coexist for years, just as marketplaces and open-web commerce coexist today.

But regardless of which standards ultimately win, the direction of commerce is becoming more clear, and it seems that it will be guided by what AI agents can interpret and understand just as much as it’s guided by what appeals to human shoppers.

Choosing the right Product Information Management (PIM) system can make or break your ability to deliver compelling product experiences across channels, especially in 2026.

As businesses expand across marketplaces, eCommerce platforms, retail stores, and emerging AI-driven channels, managing product data manually or across disconnected systems simply doesn’t hold up. Inconsistent or incomplete product information can frustrate human shoppers, but can also straight up disqualify your products from AI-driven recommendations and automated buying flows as agentic commerce becomes more and more commonplace.

In 2026 and beyond, the best PIM systems go beyond centralizing product data. They enable AI-powered enrichment, supplier onboarding, omnichannel syndication, and structured data that is machine-readable.

Choosing the right PIM, then, is a strategic investment that directly impacts customer experience, operational efficiency, revenue growth, and the ability to adapt to the future of commerce.

What Is a Product Information Management (PIM) System?

Before we get too far, let’s first start with some definitions.

A Product Information Management (PIM) system is a centralized platform that collects, manages, enriches, and distributes product data across all sales and marketing channels.

At its core, a PIM acts as a single source of truth for product information, bringing together data from multiple systems like ERP (Enterprise Resource Planning), suppliers, spreadsheets, and Digital Asset Management (DAM) tools into one unified platform.

This includes:

• Product descriptions and specifications
• Technical attributes and dimensions
• SKU data
• Localization and translations
• Channel-specific content

But nowadays, a PIM does much more than store data.

Modern PIM systems should be designed to transform raw product data into rich, contextualized product experiences. They enable teams to collaborate on product content, automate enrichment processes, and ensure consistency across every touchpoint, from eCommerce sites to marketplaces to mobile apps.

As AI agents increasingly take on the role of researching and purchasing products, the structure and quality of your product data become even more critical.

Unlike human shoppers, AI agents don’t “interpret” messy or incomplete data. They rely on clean, structured, and standardized information to make decisions.

Without a PIM, product data often lives in silos, leading to inconsistencies, errors, and delays. With a PIM, businesses can streamline operations, accelerate time-to-market, and deliver the kind of high-quality product experiences that both humans and AI agents expect.

What to Look for in a PIM System

Before diving into vendors, it’s important to understand what separates a good PIM from a great one.

1. Data Management & Enrichment: A strong PIM should centralize product data and enable enrichment through validation rules, completeness scoring, and AI-driven content generation.

2. Omnichannel Syndication: Look for platforms that easily distribute product data across marketplaces, eCommerce platforms, and retail systems.

3. Workflow & Collaboration: Modern PIMs support cross-functional collaboration with approval workflows, role-based permissions, and governance controls.

4. Scalability & Flexibility: Your PIM should scale with your catalog, channels, and geographic expansion without requiring major replatforming.

5. Integrations & API-First Architecture: Seamless integrations with ERP, DAM, CMS, and eCommerce platforms are critical.

6. AI & Automation Capabilities: Leading PIM systems now include AI for content generation, translation, and data quality improvements.

7. Vendor Support & Ecosystem: The vendor’s roadmap, support quality, and partner ecosystem can be as important as the software itself.

Best PIM Systems of 2026

1. Akeneo

Akeneo has evolved from a traditional PIM into a full Product Cloud platform, focused on Product Experience (PX) and AI-driven enrichment. It enables organizations to centralize, enrich, activate, and optimize product data across all channels, with a strong emphasis on usability and collaboration.

Recent innovations have positioned Akeneo as a leader in preparing businesses for AI-driven and agentic commerce. Its 2026 Spring Release introduced the idea of the Intelligent Feedback Loop, enabling product data to continuously improve based on real-world signals, alongside responsive catalog modeling and AI Discovery Optimization. 

Additionally, Akeneo continues to invest heavily in AI, including automated attribute extraction from assets and AI-generated product descriptions, helping teams enrich data faster and at scale.

Benefits:

• Strong AI-powered enrichment and automation capabilities
• Highly user-friendly interface with collaborative workflows
• Robust ecosystem and integrations (200+ apps, major commerce platforms)
• Purpose-built for omnichannel and AI-driven commerce

Best for:
Mid-market to enterprise brands focused on delivering high-quality product experiences at scale, especially those preparing for AI-driven or agentic commerce.

2. Bluestone PIM

Bluestone PIM is a headless, API-first PIM designed specifically for composable commerce architectures. Unlike traditional monolithic PIM systems, Bluestone allows organizations to plug product data into modern, modular tech stacks.

Recent updates have focused on GraphQL-based APIs, event-driven architecture, and real-time data synchronization, enabling faster integrations with front-end applications and AI-driven services.

Benefits:

• Fully composable and headless architecture
• Real-time data delivery via APIs
• High flexibility for custom digital experiences

Best for:
Digital-first organizations and enterprises adopting composable commerce or MACH architectures.

3. Inriver

Inriver positions itself as a revenue-driving PIM, with a strong focus on product storytelling, syndication, and digital shelf optimization. It is widely used by large enterprises managing complex product ecosystems.

Recent innovations include enhanced product marketing capabilities, improved channel syndication tools, and deeper analytics to measure product performance across channels. The platform continues to invest in AI-powered content enrichment and automation.

Benefits:

• Strong syndication network and marketplace integrations
• Focus on driving revenue and reducing time-to-market
• Advanced product storytelling and merchandising tools

Best for:
Large enterprises and global brands managing complex catalogs and multiple distribution channels.

4. Pimberly

Pimberly is a SaaS PIM focused on data automation, onboarding, and enrichment, helping businesses rapidly clean and structure product data.

Recent platform updates emphasize automation-first workflows, including bulk data ingestion, automated validation, and improved supplier onboarding tools. Pimberly also continues to enhance data governance and workflow automation to reduce manual effort.

Benefits:

• Strong automation capabilities for data onboarding
• Faster time-to-value compared to many enterprise PIMs
• Clean, intuitive interface

Best for:
Retailers and distributors looking for fast implementation and improved data quality without heavy complexity.

5. Pimcore

Pimcore is an open-source platform that combines PIM, DAM, CMS, and MDM into a single solution. It’s known for its flexibility and ability to support highly customized use cases.

Recent developments include stronger support for headless and composable architectures, improved data modeling capabilities, and enhanced API performance, making it more competitive in modern tech stacks.

Benefits:

• Extremely flexible and customizable
• Combines multiple capabilities in one platform
• Open-source foundation reduces licensing costs

Best for:
Enterprises with strong in-house development teams and highly complex data or experience requirements.

6. Plytix

Plytix is a cloud-based PIM designed for simplicity, usability, and affordability, making it especially popular among SMBs.

Recent improvements include enhanced data completeness tracking, workflow automation, and improved collaboration tools, along with better integrations for eCommerce platforms like Shopify and WooCommerce.

Benefits:

• Easy to use and quick to deploy
• Affordable pricing for smaller teams
• Strong collaboration and catalog management features

Best for:
Small to mid-sized businesses looking for an accessible entry point into PIM.

7. Salsify

Salsify is a Product Experience Management (PXM) platform that combines PIM with digital shelf analytics, syndication, and content optimization.

Recent innovations include deeper digital shelf analytics, expanded retailer and marketplace syndication, and increased use of AI to optimize product content for discoverability and conversion.

Benefits:

• Strong syndication network and retailer integrations
• Focus on digital shelf performance and analytics
• Scales effectively for global brands

Best for:
Brands heavily focused on marketplaces, retail syndication, and digital shelf optimization.

8. Syndigo

Syndigo combines PIM, MDM, and content syndication into a unified platform, with a strong focus on global data distribution and compliance.

Recent enhancements include expanded global data pool connectivity, improved analytics and reporting tools, and deeper integration with retail and GDSN (Global Data Synchronization Network) ecosystems.

Benefits:

• Strong global syndication capabilities
• Advanced data governance and compliance tools
• Comprehensive enterprise-grade platform

Best for:
Enterprises needing global data synchronization, compliance, and large-scale syndication.

How to Choose the Right PIM for Your Business

There is no one-size-fits-all “best” PIM system, just the one that best aligns with your business goals, team structure, and technical ecosystem.

To make the right choice:

• Start with your business objectives (growth, efficiency, CX)
• Map your data complexity and channel strategy
• Evaluate ease of use vs. flexibility
• Consider long-term scalability and vendor support

But in 2026, there’s an additional layer to consider: how well your PIM prepares you for agentic commerce.

As AI agents increasingly influence, or even complete, purchasing decisions, your product data must be structured, contextualized, and optimized for machine consumption. This means choosing a PIM that can:

• Deliver clean, standardized, and enriched data at scale
• Support real-time updates across channels and endpoints
• Enable AI-driven enrichment and continuous data improvement
• Ensure your products are discoverable and competitive in algorithm-driven environments

Most importantly, treat your PIM as a long-term strategic investment. The right platform will position your business to thrive in a future where both humans and AI agents are making buying decisions.

Spring is a time of renewal, and for product-driven organizations, it’s a reminder that growth depends on the ability to evolve.

Today, product information must evolve as quickly as the markets it serves. New channels, shifting customer expectations, and the rise of AI-powered discovery are fundamentally changing how products are found, evaluated, and purchased. Static product data can no longer keep pace.

That’s why, as announced in Unlock Chicago, the Akeneo Spring 2026 Release introduces a new set of innovations designed to make product data more dynamic, intelligent, and actionable. These capabilities empower teams to respond faster, enrich smarter, and deliver product experiences that continuously improve over time.

At the core of this release are three key pillars, each supported by new products and features that bring this vision to life.

The Three Pillars of Akeneo’s 2026 Spring Release

1. The Intelligent Feedback Loop

Traditionally, product data has been managed as a static system of record. Teams build a data model, enrich product content, distribute it across channels, and then react when something goes wrong. 

The problem is that today’s commerce environment doesn’t wait. Markets evolve in real time, and product data needs to evolve with them.

The Intelligent Feedback Loop changes this dynamic.

Instead of relying on periodic updates and manual fixes, Akeneo transforms product data into a continuously learning system that captures signals from across the product journey and feeds them back into the PIM as actionable improvements.

Key New Features

• Responsive Catalog Modeling & Enrichment: Responsive Catalog Modeling & Enrichment enables your catalog to adapt automatically to market demands. By analyzing retailer requirements, rejection logs, and search trends, it suggests both missing attributes and the exact values needed to complete them. This eliminates manual research, reduces bottlenecks, and dramatically accelerates time-to-market, turning reactive fixes into proactive optimization.
• PX Insights – AI Discovery Optimization: Akeneo connects AI-driven discoverability insights directly to your PIM structure. Instead of vague recommendations, teams receive clear, actionable guidance on which attributes to create or refine based on how AI engines interpret their products. This bridges the gap between insight and execution, making AI optimization tangible and achievable.
• Flexible AI Sourcing – Bring Your Own LLM: Enterprises can now integrate their preferred AI providers directly into Akeneo. Whether using OpenAI, Claude, or another model, this feature ensures organizations can scale AI-driven enrichment while maintaining compliance, security, and control.
• Extension Platform: With built-in backend hosting powered by Upsun, Akeneo now enables full-stack extensibility within its ecosystem. Teams can deploy custom applications, like ERP integrations or regulatory checks, without managing external infrastructure, reducing complexity and accelerating innovation.

2. Velocity: Accelerating Time to Market

The second pillar is all about removing friction at every stage of the product lifecycle so teams can move from idea to execution faster and with greater confidence. 

In practice, this means simplifying complex workflows, reducing reliance on technical resources, and automating time-consuming processes that traditionally slow teams down. 

Whether it’s onboarding new users, enriching product data, activating new retail channels, or equipping sales teams with the right information, Akeneo’s latest innovations are designed to streamline each step. By embedding AI assistance, standardizing integrations, and making product data more accessible across the organization, our goal is to enable teams to spend less time navigating systems and more time driving growth, ultimately accelerating time-to-market and improving overall business agility.

Key New Features

• Ask Ziggy, the PIM AI Assistant: Ask Ziggy is a new AI-powered assistant embedded directly in Akeneo PIM. It provides contextual, real-time guidance on configurations, workflows, and catalog data, helping users navigate complexity and make better decisions faster. By reducing reliance on technical experts, Ask Ziggy accelerates onboarding and empowers more teams to work efficiently within the PIM.
• Activation – Custom Channels: Custom Channels revolutionize retailer onboarding by replacing manual processes with a template-driven approach. Simply upload a retailer file, and Akeneo automatically creates a reusable, governed activation channel.This dramatically reduces onboarding time, improves compliance, and eliminates the need for one-off engineering work.
• Akeneo Digital Showroom: Designed for sales teams, the Digital Showroom provides secure, on-demand access to enriched product data, complete with single sign-on, flexible exports, and engagement analytics. Sales reps can instantly answer buyer questions, prepare for meetings faster, and uncover new opportunities, all powered by consistent, high-quality product information.
• Target Plus API Connector: This certified connector enables seamless integration with Target’s exclusive third-party marketplace. By automating product syndication and order management, it ensures compliance while simplifying operations, making it easier than ever to expand into new channels.

3. Trust & Governance with Extensibility

As product data becomes more dynamic, interconnected, and increasingly powered by AI, maintaining control, security, and consistency becomes both more challenging and more critical. Without the right guardrails, faster workflows and automated enrichment can introduce risk, ranging from inconsistent brand messaging to compliance issues and data inaccuracies. 

The 2026 Spring Release addresses this by embedding governance directly into every stage of the product data lifecycle, ensuring that innovation doesn’t come at the expense of control. With capabilities like secure supplier collaboration, controlled staging environments, and AI enriched with built-in brand context, Akeneo enables organizations to scale their operations confidently. 

Teams can collaborate more openly, automate more processes, and adopt AI more broadly, while still maintaining full visibility, auditability, and alignment with brand, regulatory, and organizational standards.

Key New Features

• Secure Supplier Collaboration (Contributor Portal): The enhanced Contributor Portal enables secure, structured collaboration with suppliers. With managed accounts, controlled access, and auditable workflows, businesses can streamline product data collection while maintaining full governance.
• Staging Catalog: The new Staging Catalog provides visibility into supplier-submitted data before it enters the live catalog. This creates a controlled environment for validation and approval, protecting data integrity while improving transparency.
• GenAI – Brand Context Injection: Akeneo’s GenAI capabilities now incorporate structured brand data (such as tone of voice and regulatory requirements) directly into content generation. This ensures AI-generated content is not only fast, but also accurate, compliant, and aligned with brand standards across every market.
• Activation – Channel Readiness  Guide: The Channel Readiness  Guide centralizes marketplace requirements, giving teams a clear understanding of what data is needed before activation begins. By enabling proactive data modeling, it reduces rework, improves alignment across teams, and accelerates time-to-first activation.

Turn Product Data Into Your Competitive Advantage

Spring is a season of progress; a time to refresh, reset, and set the stage for what’s next. And in many ways, that’s exactly what the Akeneo Spring Release is all about.

It’s about giving your product data a fresh start, transforming it from something you manage into something that actively works for you.

Because when your product information can adapt, improve, and scale alongside your business, everything else starts to move a little faster, too.

Ready to see what’s new? Explore all the innovations in the Akeneo 2026 Spring Release and discover how you can turn your product data into a true engine for growth. Or, if you’re already convinced, reach out to an Akeneo expert directly to get started today.

Agentic commerce is moving fast. Every week seems to bring a new feature, a new protocol, a new launch, and a new prediction about how buying behavior is about to change forever. 

For brands, that creates a real problem. It’s hard enough to stay informed without getting distracted by the sheer volume of noise. The important thing is not to track every release, it’s to stay close to what is actually changing customer behavior. 

The customer still sits at the center of the buying journey. Agents will increasingly support, shape, and eventually automate parts of the commerce journey, but they are still working on behalf of a human being with preferences, doubts, habits and limits. To win in this environment, you need to influence both the system and the human behind it. 

This is also why it is important to separate real shifts from hype. 

Platform Innovation vs. Consumer Trust

At the moment, platform innovation is moving faster than user trust. New capabilities appear all the time, and some of them are genuinely important, but many look more disruptive in a product demo than they do in the real world.

According to Forrester research, while one-third of younger consumers use ChatGPT for product discovery, completing purchases within answer engines remains their least-adopted behavior. The gap is clear: consumers trust AI to browse and recommend, but not yet to buy on their behalf. This explains why ChatGPT’s agentic checkout, launched with much fanfare in December 2025, was quietly pulled just three months later.

Helping someone research a product is one thing. Ensuring they have enough trust to allow an agent to buy it for them with confidence is another. Shopping and buying are not the same. One is exploratory, while the other involves trust, intent, retailer preference, fulfillment expectations, and a willingness to hand over control.

That’s why fully agentic commerce is harder than many people first assumed.

At ChannelSight, we have spent more than a decade working at the intersection of brands, retailers, and consumers. One thing we have learned over that time is that the buying journey is never static. It needs constant optimization; it’s not something you configure once and forget. It’s shaped just as much by confidence and trust as it is by technology. 

What the Data Shows

Across the market, eCommerce traffic patterns are beginning to shift as more product discovery happens inside conversational interfaces rather than along the traditional search-to-shop path. But that shift has not yet flowed through to lower-funnel purchase behavior in the same way. 

That tells us that while discovery is changing quickly, buying behavior is changing more slowly. The technology may be capable of more, but consumers are not automatically ready to follow. 

So, while agentic commerce is absolutely real, it is still early. There will be plenty of developments that look like major turning points in the moment, but not all of them will stick. Behavior is what decides that, not headlines. 

What Should Brands Actually Do Now?

1. Get Your Product Content In Order

If your product data is incomplete, inconsistent, or poorly syndicated across retail channels, you’re making yourself harder to discover in exactly the places that are becoming more important. In an LLM-driven world, structured and reliable content matters even more.

This is where having a strong product information foundation becomes critical. Akeneo Product Information Management (PIM) helps brands centralize, enrich, and standardize product data so it’s complete, consistent, and ready to be consumed by both humans and machines. Instead of fragmented or conflicting information across channels, you create a single, reliable product record that can be easily understood, indexed, and surfaced by AI-driven systems.

Akeneo Activation extends this by ensuring that enriched product content is not just well-managed, but effectively distributed. It enables brands to syndicate product information across retailer sites, marketplaces, and digital touchpoints, helping ensure that wherever agents or customers are searching, the right content is present, accurate, and optimized. 

In a world where discovery is increasingly mediated by algorithms and agents, being visible depends on being both structured and everywhere your customers, and their agents, are looking.

2. Pay Close Attention to Discoverability

Product discovery is clearly moving towards conversational and LLM-led environments. Whether that becomes fully agentic will depend in part on how relationships develop between LLMs, retailers, and commerce platforms, but the immediate priority remains clear: your products need to be visible, understandable, and easy to recommend in these environments.

ChannelSight’s Agent Discoverability tool helps brands see which products and retailers are actually being surfaced across LLM experiences, where the gaps are, and where action is needed:

3. Rethink How You Create Content

Aspirational product pages still matter, but they’re no longer enough on their own. Brands need content that answers real questions, reflects actual use cases, makes product differences clear, and gives systems something useful to interpret and surface.

That means:

• Sharper metadata and better-structured product information
• Stronger and more frequent customer reviews
• Improved FAQ and Q&A content
• More clarity around what makes your product different and why someone should choose it
• Content written to answer questions, not just inspire

Consider incentivizing reviews more aggressively (cashback promotions, etc.) and adapting your content strategy to focus on Q&A-style content rather than purely aspirational product pages. Hype up your product USPs and what differentiates them from competitors—this is what LLMs will surface when consumers ask comparison questions.

In short, the brands that do well here will be the ones that are easiest to understand.

How ChannelSight Is Helping Brands Navigate This Shift

At ChannelSight, we are watching these shifts closely through traffic patterns across our clients owned and earned channels, and through the tools we are building to help brands understand and improve discoverability in AI-driven environments. 

We have also launched Conversational Commerce as part of the ChannelSight AI suite. It allows brands to create product-specific and product-agnostic brand interactions through chat interfaces, then connect those conversations directly to relevant purchase journeys. That reduces friction, limits distraction, and keeps the user moving. 

And these experiences don’t have to begin on a website—they can be triggered from email, social media, and display advertising too. Think of it like your brand store growing by 10,000 square meters and becoming conversational.

Stay Focused on What Actually Matters

The space will keep moving quickly. There will be more launches, more claims, and more shiny distractions. But the brands that come out ahead will not be the ones reacting to every announcement. They will be the ones that stay close to real customer behavior, invest in discoverability, and focus on the parts of the buying journey that genuinely matter. 

Agentic commerce is here to stay. Every platform wants to show how quickly the buying journey is changing, and every week seems to bring a new reason for brands to rethink their strategy. 

That is the real opportunity. The job right now is not to get swept up in every new release. It is to work out what is actually changing customer behavior, and what is still just potentially dressed up as inevitability. 

The brands that understand that difference—and act accordingly—will be the ones that thrive.

Like millions worldwide who eagerly anticipate their favorite holidays, we at Akeneo share a similar excitement for our flagship event: Unlock. 

From April 13 – 14, 2026, Akeneo will welcome customers, prospects, and PX experts to the “Windy City”, Chicago. Located at the famous Willis tower, Unlock showcases the latest innovations in PX through dynamic keynote presentations, hands-on technical workshops and expert-led practice sessions, focusing on the theme of the event: The Great Restack: Welcome to the New Age of Commerce.

Amidst this “New Age of Commerce”, many brands over the last couple of years have faced challenges with AI integration, a centralized system for product data, and streamlined communication. All of these issues, if not tackled, can have a hidden cost, affecting your brand further down the line. 

Steelcase, a global leader in office furniture and accessories, knows this challenge well. Before transforming their product data strategy with Akeneo Product Cloud, Steelcase faced many of the same operational hurdles that countless organizations encounter today.

The Challenge: Untangling the Complex Web of Product Data

For more than a century, Steelcase has led the office furniture industry with insight-driven design and a commitment to creating inspiring, people-centered workplaces that boost productivity and engagement.

Today, Steelcase operates at the intersection of architecture, furniture, and workplace technology, positioning itself as a problem-solver for organizations building the offices of the future.

But even the most innovative companies can face operational friction when their product data ecosystem becomes too complex.

Before adopting Akeneo Product Cloud, Steelcase encountered several familiar challenges:

• Manual, spreadsheet-based management of surface material data, which studies suggest wastes up to 12 hours per week hunting down information, leading to as much as 30% of total revenue loss due to inefficiency and misalignment. 
• Inefficiencies in linking digital assets to product finishes. Reflecting how incorrect product information is the reason 40% of shoppers return their items.
• Time-consuming updates across multi-regional finish records. With a PIM, managing product data is up to 6× faster than in a spreadsheet.
• Managing supplier data involved a significantly high manual workload, which in business

These challenges made it clear that Steelcase needed a more scalable and centralized way to manage product information.

The Turning Point: Flexibility & Scalability with Akeneo

By implementing Akeneo Product Cloud, Steelcase transformed fragmented product information into a structured, scalable data ecosystem.

The impact was immediate, and Steelcase has been able to:

1. Create a centralized system for product data

After implementing Akeneo PIM and Akeneo’s Supplier Data Manager (SDM), Steelcase was able to create a streamlined, single source of truth for all of their product data. This allowed Steelcase to achieve a 62% reduction in annual time spent loading supplier data, from 33.36 hours to just 12.14 hours. After years of “entirely dependent on IT”, Steelcase was now able to use the Akeneo system to cleanse and validate data, for the best results possible.

2. Automate asset linking for images, textures, and color representations

While it sounds like a simple issue, linking digital assets to the right products once requires hundreds of manual hours which are spent better in other tasks. But with Akeneo, Steelcase saw a 63.64% decrease in time spent per product family, cutting it from 22 minutes to just 8 minutes. 

3. Optimize time-to-market for product releases

One of the most impactful benefits of Akeneo Product Cloud was the dramatic improvement in product launch speed.

With automated workflows and improved data accuracy, Steelcase achieved:

• 63.61% reduction in product release hours
• Reduced release preparation time from 16.68 hours to just 6.07 hours

This increased agility allows Steelcase to respond faster to market demands, minimize missed sales opportunities, and deliver innovative products to customers sooner.

The result is a mature, scalable product data foundation that enables Steelcase to move faster, collaborate more effectively, and deliver better product experiences to customers.

Steelcase to Share Their Story at Unlock 2026

Steelcase will be sharing their story live at Unlock 2026, offering firsthand insights into:

• What their product data landscape looked like before Akeneo
• How their workflows transformed after implementation
• How they see AI shaping product data and commerce in the years ahead

And Steelcase is just one of many conversations happening in Chicago.

Other sessions at Unlock 2026 will explore:

• Product keynotes on the future of Akeneo Product Cloud and the vision behind The Great Restack
• Hands-on technical workshops covering data modeling, governance, and enrichment workflows
• Expert-led keynote sessions on preparing for the future of agentic commerce

If traveling to Chicago isn’t possible, you can still participate through Unlock Digital, a completely free virtual event that brings the same expert insights and customer stories online taking place live from April 21-22.

You can register now for both Unlock Chicago and Unlock Digital.

SAP S/4HANA and SAP Commerce Cloud are powerful, enterprise-grade platforms. They’re designed to handle massive volumes of operational and commerce data, support complex global business models, and scale as organizations grow.

But anyone who has worked in a real SAP environment knows the truth: integrating SAP systems into a modern product data architecture is rarely simple.

Each SAP landscape is different. Data models vary. Customizations stack up over time. And product data doesn’t live neatly in one place; it flows between ERP systems, PIM solutions, commerce platforms, and dozens of downstream channels. There’s no universal “plug-and-play” integration that works for everyone.

That’s exactly what Akeneo’s SAP Accelerators are built for.

Rather than offering rigid, black-box connectors, Akeneo provides accelerators that reduce initial integration friction while preserving the flexibility enterprises need to evolve, customize, and scale over time.

Let’s take a closer look at how they work, who they’re designed for, and why they matter.

SAP Complexity and the Product Data Challenge

SAP S/4HANA and SAP Commerce Cloud both rely on rich, highly structured data models. That power comes with complexity.

No two SAP implementations look exactly the same as business rules, extensions, custom objects, and industry-specific requirements all shape how product data is stored and used. As a result, integrating a PIM into an SAP ecosystem is never just about moving data from Point A to Point B.

A successful integration requires careful orchestration between:

• ERP systems that manage operational and transactional data
• PIM systems that enrich, localize, and structure product information
• Commerce platforms that activate that data across digital touchpoints

Shortcuts and overly rigid connectors might promise speed, but they often create long-term pain that limit flexibility, make governance harder, and slow innovation down the line.

Akeneo’s SAP Accelerators take a different approach.

What Are Akeneo’s SAP Accelerators?

Akeneo’s SAP Accelerators are integration blueprints designed to help teams connect SAP environments with Akeneo Product Cloud faster without locking them into inflexible workflows.

They’re built using SAP-native technologies and follow SAP best practices, giving enterprises confidence that integrations will scale, perform, and remain governable over time.

SAP S/4HANA Accelerators

The SAP S/4HANA Accelerators are delivered as prebuilt iFlows on SAP Business Technology Platform (BTP), using SAP Integration Suite.

Importantly, these are not monolithic connectors. They’re reusable integration artifacts that teams can configure, extend, and adapt to their own landscape.

Key capabilities include:

• Support for both batch synchronization (ERP → PIM) and event-driven flows (PIM → ERP)
• Message mapping logic aligned with common SAP data patterns
• Workflow templates for scheduling, orchestration, authentication, and filtering

This gives teams flexibility to choose the integration model that fits their business, whether they need high-volume batch processing, near real-time updates, or a mix of both.

Learn more about Akeneo’s SAP S/4 HANA Accelerators

SAP Commerce Cloud Accelerator

The SAP Commerce Cloud Accelerator provides a low-code option to synchronize rich, localized product information from Akeneo PIM into SAP Commerce Cloud.

Built on SAP Integration Suite and integrated into the SAP BTP toolkit, it enables technical teams to:

• Configure and transform product data to meet complex commerce requirements
• Reduce custom development while maintaining control over data flows
• Activate enriched product experiences faster across commerce channels

Rather than reinventing the wheel, teams get a clear blueprint that accelerates integration while leaving room for customization.

Learn more about Akeneo’s SAP Commerce Cloud Accelerator

Why Akeneo’s Accelerator Approach Works

Akeneo’s SAP Accelerators deliver value not by oversimplifying SAP, but by respecting its complexity.

Here’s what that means in practice.

1. Reduced Initial Friction

Building SAP integrations from scratch is time-consuming and resource-intensive. The accelerators provide a ready-made foundation so teams don’t have to start with a blank slate.

By handling core integration patterns, mappings, and workflows upfront, they significantly reduce manual effort and early-stage complexity, which is especially helpful when dealing with SAP’s intricate data structures.

2. Flexibility Built In

Because these accelerators are built on SAP’s native integration tools, teams retain full control over integration logic, governance, and extensions. As business requirements evolve, integrations can evolve with them without ripping everything out and starting over.

3. SAP-Native by Design

By leveraging SAP BTP and SAP Integration Suite, the accelerators ensure:

• Enterprise-grade scalability and security
• Alignment with SAP best practices
• Compatibility with existing SAP governance models

This SAP-native approach makes life easier for IT teams and system integrators, and reinforces Akeneo’s role as a true partner within the SAP ecosystem.

4. Faster Time-to-Market

With integration plumbing accelerated, teams can focus on what actually drives value: enriching product data, improving customer experiences, and launching new products faster.

When enriched product information flows smoothly into SAP Commerce Cloud and downstream channels, brands can activate compelling product experiences without unnecessary delays.

Start Smart & Scale with Confidence

The team here at Akeneo works closely with SAP and ecosystem partners to deliver solutions that reflect real enterprise realities. Instead of locking customers into rigid connectors, the accelerator approach embeds integration logic where customers and integrators can see it, govern it, and customize it.

Our goal is to provide a smart, SAP-native starting point for integrating SAP S/4HANA and SAP Commerce Cloud with Akeneo Product Cloud in order to reduce friction, preserve flexibility, and help teams scale confidently without sacrificing control.

For enterprises navigating complex SAP landscapes, that balance makes all the difference.

If you’re interested in learning more about Akeneo’s SAP Accelerators, you can learn more here, or reach out to an Akeneo expert today to get started.

Every team starts with good intentions. 

You configure your Product Information Management (PIM) system, set up workflows, assign roles, and everything runs smoothly. But as your business grows, things change. New roles appear. Processes evolve. And suddenly, the standard interface no longer reflects how people actually work.

That’s the moment Custom Components were built for.

Launched as part of Akeneo’s Winter Release, Custom Components give customers and partners a powerful new way to extend the Akeneo PIM interface without breaking governance, compromising security, or waiting for core product changes. They let you shape the PIM around real workflows, real roles, and real business needs.

What are Custom Components?

Custom Components are the most powerful extension type within Akeneo’s Extension Framework. They are self-contained JavaScript applications built using the Akeneo Extension SDK and run securely inside the PIM.

Unlike traditional customizations or external tools, Custom Components:

• Live directly inside Akeneo
• Feel completely native to users
• Have authenticated access to PIM APIs
• Doesn’t separate front-end management

This means teams can build rich, interactive experiences, such as dashboards, data visualizations, guided workflows, or integrations with other systems, without pulling users out of the PIM or creating fragile workarounds.

Why Custom Components are important

Most teams eventually outgrow standard configuration.

At first, configuration works well. But over time, cracks appear. The UI no longer matches daily workflows. Different roles need different views. Teams need visibility into data from other systems, but switching tools slows everything down.

When every request turns into “Can this go on the roadmap?” or “Can we hack around this?”, productivity suffers.

Custom Components allow teams to extend the Akeneo UI where it matters most, without touching the core product or creating technical debt. The result is a more flexible platform that adapts to your business, not the other way around.

Built for real workflows, not one-size-fits-all

No two teams work the same way. And your product platform shouldn’t force them to.

Custom Components allow companies to build purpose-driven interfaces directly inside Akeneo. You can embed external data, apply industry-specific logic, and surface the right information at the right time, all within the PIM context.

This enables:

• Role-based experiences for product, sales, and operations teams
• Industry-specific workflows without breaking governance
• Interfaces that reflect how work actually gets done

Bringing external data into the PIM securely

Modern product workflows rarely live in a single system.

With Custom Components, teams can integrate data from external systems such as Enterprise Resource Planning (ERP) platforms, Digital Asset Management (DAM) systems, or other business tools via APIs, and bring that data directly into the PIM experience.

This allows teams to:

• View external data in context
• Reduce tool switching
• Make faster, better-informed decisions,

all while maintaining secure access and consistent permissions.

What can you build with Custom Components?

Custom Components unlock a wide range of high-value use cases, including:

• Dashboards that surface KPIs and product health metrics
• Guided workflows that help users complete tasks faster and more accurately
• Specialized interfaces for specific roles or business units
• Integrated views that blend PIM data with external systems

These are the types of capabilities customers consistently ask for and now they can be delivered faster, with less risk, and without compromising the Akeneo user experience.

A More Flexible Future for Product Operations

Custom Components deepen Akeneo’s role as the central hub for connected product experiences.

They give customers and partners the freedom to build tailored UI experiences that match how their business actually operates, without sacrificing security, governance, or usability.

By reducing tool switching, supporting role-based workflows, and enabling richer integrations, Custom Components help teams move faster, work smarter, and get more value from Akeneo as the foundation of their product operations.

Retail has invested billions in digital transformation over the past few years. New channels, new platforms, new experiences, all designed to meet rising customer expectations. And yet, customer frustration is growing, not shrinking.

Why is this the case? The modern shopping experience isn’t failing because retailers lack ambition or technology. It’s failing because the foundation beneath it all, product information, isn’t strong enough.

That was the core theme of Akeneo’s NRF session, Winning the Modern Buyer: How a Strong Digital Foundation Delivers the Customer Experiences Shoppers Expect. The conversation brought together leaders from CITY Furniture, The Paper Store, and Akeneo to explore how trust is built (or broken), why product data has moved from back-office concern to executive priority, and how retailers can confidently prepare for an AI-powered future.

The modern shopping experience is in crisis

To set the stage, Akeneo CEO Romain Fouache began with a sobering reality check. Consumer dissatisfaction with the completeness and quality of product information has more than doubled in just two years, climbing from 13% to over 30%. Even more alarming, two-thirds of global shoppers say they’ve abandoned a purchase entirely because product information was missing or incorrect.

These are everyday moments where trust quietly erodes. On a product detail page with vague specifications, in a marketplace listing that contradicts a brand site, or during a store visit that doesn’t match what a shopper researched online. As expectations rise, “good enough” product information is no longer good enough at all.

Where trust breaks first, and why it matters

When asked where trust breaks down most often in today’s customer journey, both retailers pointed to moments where accuracy and clarity are non-negotiable.

For CITY Furniture, the stakes are especially high. Selling big-ticket, bulky items means shoppers need absolute confidence before committing. Even small inconsistencies in dimensions, materials, or care instructions can immediately disqualify a product, or the retailer itself. Product detail pages have become a critical make-or-break moment, especially when nearly 80% of shoppers research online before ever stepping into a store.

The Paper Store faces a different, but equally demanding, challenge. With a massive assortment, deep size and color variations, and extreme seasonality, there is zero margin for error. Nearly half of their annual revenue is generated in a short window between Thanksgiving and Christmas. If product information is missing, inaccurate, or poorly merchandised during that window, the opportunity is simply gone. The downstream impact touches everything — conversion, fulfillment, store traffic, customer trust, and operational efficiency.

In both cases, broken product experiences hurt sales and quietly chip away at brand equity and long-term loyalty.

When product data stopped being “just an IT problem”

A major turning point for both organizations was realizing that product data could no longer live solely in the back office or be treated as a technical afterthought. Product information now shapes discovery, conversion, fulfillment, customer service, and even in-store experiences.

At CITY Furniture, product data initially emerged as an eCommerce foundation. Over time, it evolved into a centralized data library supporting the entire organization as the business modernizes. While IT still plays a critical role in architecture, ownership of product content increasingly sits at the intersection of digital commerce and merchandising, where customer impact is felt most directly.

At The Paper Store, the shift was even more stark. Relying solely on a merchandising system proved insufficient for today’s omnichannel demands. The lack of structured, enriched product information made it harder to execute quickly, scale efficiently, or deliver consistent experiences across channels. Product data became a business imperative, not a technical deliverable.

What stood out most was the shared acknowledgment that product data isn’t flashy. It doesn’t always win internal popularity contests. But without executive advocacy and clear ownership, even the most sophisticated tech stacks fall short.

Product data as a measurable growth lever

Once treated as a strategic asset, the impact of improved product information becomes tangible. Both retailers emphasized that while metrics like add-to-cart and conversion rate are useful indicators, the true value of product data extends far beyond a single KPI.

Richer, more accurate product content reduces returns, lowers customer support burden, improves discoverability, and drives higher-quality store traffic. It also enables better upsell and cross-sell opportunities, especially in omnichannel journeys where customers move fluidly between digital and physical environments.

Turning trusted data into revenue and AI confidence

The session also looked forward, especially as AI continues to dominate retail conversations. The message was clear: AI doesn’t fix broken foundations. It amplifies them.

Shoppers are already signaling what they value. On average, they’re willing to pay up to 25% more for products that come with complete, high-quality information. Trust and clarity now define value just as much as price.

AI can absolutely enhance discovery, personalization, and customer support, but only when it’s powered by clean, structured data. Poor data hygiene leads to hallucinations, misinformation, and inconsistent experiences that undermine trust even faster than static errors.

Practical AI use cases are already delivering value today, including:

• AI-powered chat experiences that answer real product questions beyond a short PDP description
• Smarter comparisons that help shoppers understand differences between similar products
• Faster enrichment and optimization of product content at scale

However, both retailers cautioned against rushing ahead without addressing data readiness first. Governance, integration, and enrichment must come before experimentation at scale.

Advice for retail leaders starting the journey

The NRF session closed with practical advice for leaders who know they need to evolve but aren’t sure where to begin.

First, invest in data and in the people who steward it. Product data teams must understand not only how information is structured, but how it’s consumed by customers, channels, and AI systems. This work will look very different in the coming years, and adaptability is key.

Second, secure executive buy-in early. Product data transformation requires organizational commitment, not temporary fixes or side projects. Governance, standards, and accountability are foundational to long-term success.

Finally, choose the right partners. Retailers don’t have to solve these challenges alone. Learning from peers, working with experienced technology partners, and building phased, realistic roadmaps can accelerate progress while reducing risk.

The foundation matters more than ever

The modern buyer is demanding consistency, clarity, and confidence. As this NRF session made clear, product information sits at the center of every meaningful shopping experience. When the foundation is strong, retailers can move faster, innovate with confidence, and meet customers wherever they are. When it’s weak, no amount of technology can compensate.

Winning the modern buyer starts with getting the basics right, and treating product data not as an afterthought, but as the foundation of commerce itself.

Watch the full NRF session here!

If your organization builds, buys, or uses AI, you’ve probably heard about the EU AI Act by now. 

It’s the European Union’s landmark regulation designed to make AI safer and more trustworthy, while still supporting innovation. Much like the General Data Protection Regulation (GDPR), the EU AI Act sets common rules across EU member states and applies far beyond Europe’s borders. Even if your company isn’t headquartered in the EU, the Act can still affect you if your AI systems are used by EU-based customers, employees, or partners – or if their outputs impact people in the EU.

For many organizations, the EU AI Act can feel overwhelming at first glance. It’s long, detailed, and full of legal terminology. But at its core, the regulation is built around a simple idea: the higher the risk an AI system poses to people, the greater the responsibility placed on the organization using or providing it.

Here’s a practical, friendly guide to what it is, what it requires, who’s impacted, how to prepare, and what happens if you ignore it.

What is the EU AI Act?

The EU AI Act is a comprehensive regulation introduced by the European Union to govern how artificial intelligence systems are developed, deployed, and used. Its purpose is to ensure AI technologies are safe, transparent, and aligned with fundamental human rights, while still encouraging innovation and economic growth.

Rather than treating all AI the same, the EU AI Act recognizes that not all AI systems pose the same level of risk. A product recommendation engine does not carry the same potential consequences as an AI system used to screen job applicants or assess creditworthiness. Because of this, the regulation takes a risk-based approach, tailoring requirements based on how much impact an AI system could have on individuals and society.

Importantly, the EU AI Act applies not only to organizations based in the EU, but also to any organization that places an AI system on the EU market, uses AI systems in the EU, or produces AI outputs that affect people in the EU. This means global companies, much like with GDPR, cannot ignore the regulation simply because they are headquartered elsewhere.

At a high level, the EU AI Act groups AI systems into three main risk categories, each with its own expectations and obligations:

1. Unacceptable risk

At the highest level are AI systems considered to pose an unacceptable risk to people’s rights and freedoms. These systems are banned outright because their potential for harm is deemed too great to mitigate through safeguards alone. Examples include AI systems that:

• Manipulate or exploit vulnerable individuals in a way that causes harm
• Enable social scoring by governments or organizations in ways that unjustly disadvantage people
• Use certain types of biometric identification or categorization without appropriate legal justification
• Infer sensitive personal characteristics (such as beliefs or orientation) from biometric data in prohibited contexts

2. High-risk

These AI systems are allowed to exist and be used, but only if organizations meet strict requirements designed to reduce the likelihood of harm. An AI system is typically considered high risk if it plays a role in decisions that can significantly affect someone’s life, opportunities, or safety. This includes areas such as:

• Recruitment, employee management, and performance evaluation
• Education and vocational training
• Access to essential services like credit, insurance, or healthcare
• Law enforcement, border control, and migration management
• Safety components of regulated products and critical infrastructure

3. Limited and minimal risk

Most AI systems fall into the limited or minimal risk category. These are systems that do not significantly affect people’s rights or safety, but may still require transparency so individuals understand when AI is being used. Examples would include: 

• Users may need to be informed when they are interacting with an AI system rather than a human
• AI-generated or manipulated content, such as synthetic images, audio, or text, may need to be clearly disclosed
• Certain emotion recognition or biometric categorization systems must be communicated to users in advance

Who is impacted by the EU AI Act?

At a high level, the EU AI Act applies to any organization that develops, sells, deploys, or benefits from AI systems that are used in, or have an impact on, the European Union. That includes companies based both inside and outside the EU.

To make this easier to understand, the regulation defines several key roles. An organization may fall into one or multiple of the following roles:

1. AI providers: organizations that build or offer AI systems

Providers are organizations that develop an AI system or general-purpose AI model and place it on the market or put it into service under their own name or brand.

This includes:

• Software vendors building AI-powered products
• Organizations fine-tuning or adapting existing models and offering them as part of their own solution
• Companies embedding AI into hardware or digital products they sell

Providers carry the largest compliance burden, particularly for high-risk AI systems. They are responsible for ensuring the system meets regulatory requirements before it reaches customers, including risk management, technical documentation, testing, and ongoing monitoring.

If you sell AI-enabled software or embed AI into your products, even if it’s built on top of third-party models, you may be considered a provider under the AI Act.

2. AI deployers: organizations that use AI in their operations

Deployers are organizations that use AI systems as part of their internal processes or customer-facing operations. This is where many non-technical businesses are impacted.

Deployers include organizations using AI for:

• Hiring, performance management, or workforce analytics
• Customer support chatbots or virtual assistants
• Fraud detection, credit scoring, or risk assessment
• Personalization, recommendations, or dynamic pricing
• Content generation, translation, or product information enrichment

Even if the AI system is purchased from a vendor, deployers are still responsible for how it is used. This includes ensuring the system is applied appropriately, that human oversight is in place when required, and that outputs are monitored for potential risks or errors.

In other words, “we didn’t build it” is not a free pass under the EU AI Act.

3. Importers and distributors: bringing AI into the EU market

These are organizations that import or distribute AI systems within the EU also have responsibilities, including importers who place AI systems from non-EU providers onto the EU market and distributors who make AI systems available in the EU without modifying them

While these roles don’t carry the same level of responsibility as providers, they are still expected to verify that AI systems meet basic compliance requirements and to cooperate with authorities if issues arise.

This is especially relevant for global organizations that sell AI-powered tools across regions.

4. Product manufacturers: AI embedded in physical or regulated products

The AI Act also impacts manufacturers that integrate AI into physical products, such as:

• Consumer electronics
• Medical devices
• Industrial equipment
• Automotive systems

If AI is part of a product’s functionality, particularly in safety-related or regulated contexts, the manufacturer may be treated as the AI provider and must meet corresponding obligations.

This is especially important for organizations combining software, hardware, and AI into a single product experience.

5. General-purpose AI (GPAI) providers

The regulation introduces specific obligations for general-purpose AI models, which are designed to be adapted across many downstream use cases.

Organizations providing these models, whether proprietary or open source, must meet transparency and documentation requirements, particularly if the model poses systemic risk due to its scale or capabilities.

Even if you don’t sell a finished AI “application,” providing a model that others build on can still bring you into scope.

What are the key requirements for the EU AI Act?

1. For high-risk AI systems (providers)

High-risk providers should expect obligations such as:

• A continuous risk management system across the AI lifecycle (identify, evaluate, mitigate, test, monitor)
• Strong data and data governance expectations (data quality and appropriateness for intended purpose)
• Technical documentation and record-keeping/logging to demonstrate compliance and support audits
• Clear instructions for use for downstream deployers
• Designed-in human oversight
• Appropriate levels of accuracy, robustness, and cybersecurity

2. For high-risk AI systems (deployers)

Deployers (users of the system) also have real responsibilities, including:

• Use the system according to instructions, with human oversight in place
• Ensure input data is relevant for the intended purpose
• Monitor operation and take action if risks emerge
• Keep logs (where applicable) and cooperate with providers/authorities if issues arise

3. Transparency obligations

Depending on the system, organizations may need to:

• Tell people when they’re interacting with an AI system (unless obvious or exempted)
• Disclose/label AI-generated or AI-manipulated content (including deepfakes, and certain generated text rules)
• Inform people when emotion recognition or biometric categorization is used (with specific exemptions mainly tied to lawful criminal justice uses.

4. General-purpose AI (GPAI) model obligations

If you provide a general-purpose AI model (the kind that can be integrated into many downstream applications), you’ll need to:

• Maintain technical documentation (including training/testing and evaluation information)
• Provide information to downstream providers integrating your model
• Put in place a copyright policy
• Publish a public summary of training data (at the level required by the Act)
• Note: open-source GPAI models may have partial exemptions, except where “systemic risk” rules apply

What are the penalties for non-compliance?

Penalties are designed to be “effective, proportionate and dissuasive,” and Member States set enforcement details, but the AI Act sets maximum administrative fines that can be very large:

• Up to €35 million or 7% of worldwide annual turnover (whichever is higher) for violating prohibited practices
• Up to €15 million or 3% of worldwide annual turnover for violating key obligations (including many provider/deployer duties and transparency obligations)
• Up to €7.5 million or 1% of worldwide annual turnover for supplying incorrect, incomplete, or misleading information to authorities

Beyond fines, regulators can also require corrective actions and restrictions on systems, and the reputational risk can be significant.

When does The EU AI Act go into effect?

The AI Act rolls out in phases. A helpful high-level timeline from the European Commission’s AI Act Service Desk is:

• 2 Feb 2025: General provisions (including definitions and AI literacy) + prohibitions apply
• 2 Aug 2025: Rules for general-purpose AI (GPAI) apply; governance structures and national penalty regimes should be in place
• 2 Aug 2026: “Majority of rules” apply; high-risk AI systems in Annex III and transparency rules begin applying; enforcement starts
• 2 Aug 2027: Rules for high-risk AI embedded in regulated products apply 

How organizations can prepare for the EU AI Act

Preparing for the EU AI Act doesn’t require hitting pause on innovation or overhauling everything overnight. For most organizations, readiness starts with visibility and structure, not perfection. The goal is to understand where AI exists in your organization, how it’s being used, and what level of responsibility comes with each use case.

1. Build an AI inventory (yes, even the “small” stuff)

A strong first step is creating an AI inventory. This means identifying every AI system your organization builds, buys, embeds, or uses, including tools that might not immediately feel “high risk,” such as marketing personalization platforms, content generation tools, search and recommendation engines, customer support chatbots, fraud detection systems, or demand forecasting solutions. 

Many organizations are surprised by how much AI they already rely on once they map it out.

2. Classify each use case by risk tier

From there, each AI system should be evaluated based on its intended purpose, who it affects, and the potential impact of its outputs. This allows you to classify systems according to the AI Act’s risk categories and identify where obligations may apply. 

At the same time, it’s important to clarify your role for each system, whether you are acting as a provider, deployer, importer, or some combination of the three. This step is critical, as obligations differ depending on the role you play.

3. Establish AI governance and evaluation practices

Once visibility is established, preparation becomes an exercise in governance and consistency. Organizations should define clear ownership for AI-related decisions, including risk assessments, vendor selection, incident escalation, and documentation. 

This often cuts across teams, and benefits from a shared framework rather than siloed decision-making. Building basic AI literacy across teams is also essential, as the regulation expects organizations to understand how their AI systems function and where risks may arise.

4. Operationalize transparency

From an operational standpoint, many AI Act requirements align with best practices organizations may already be working toward. Strengthening data governance, documenting training and evaluation processes, implementing human oversight where decisions have meaningful consequences, and monitoring AI performance over time all help reduce risk while improving system quality. 

Transparency plays a key role, and organizations should be prepared to clearly communicate when AI is being used, especially in customer-facing or content-generating scenarios.

5. Get serious about vendor and model governance

Finally, vendor and partner management becomes increasingly important under the EU AI Act. Organizations should be prepared to ask AI vendors for documentation, compliance assurances, and clarity on how models are trained and governed. 

Even when AI is sourced externally, responsibility for its use does not disappear. Treating AI governance as part of standard procurement and risk management processes helps avoid surprises later.

The most important thing to remember is this: compliance is a journey, not a single milestone. Organizations that start early by building visibility, assigning responsibility, and embedding governance into everyday workflows will be far better positioned to meet regulatory expectations without slowing down innovation.

Final thoughts on the EU AI Act

The EU AI Act can feel intimidating because it’s comprehensive, but it’s also very “programmable” from an operational standpoint. If you can inventory systems, classify risk, document decisions, and put clear governance around high-impact use cases, you’re already most of the way there.

If you want to learn more about how to prepare for the EU AI Act, you can check out the Gartner® report, Getting Ready for the EU AI Act, Phase 1: Discover & Catalog.

Gartner, Getting Ready for the EU AI Act, Phase 1: Discover & Catalog, Nader Henein, Gabriele Rigon, 28 October 2025. 

Gartner is a trademark of Gartner, Inc. and/or its affiliates.

Chicago knows a thing or two about doing things at scale, from towering architecture and legendary sports teams to deep-dish pizza that’s anything but shallow. On April 13–14, 2026, that same bigger-and-better energy comes to Convene Willis Tower as more than 250 eCommerce and Product Experience professionals gather for Unlock 2026, the leading U.S. event dedicated entirely to Product Experience.

Hosted by the team here at Akeneo, Unlock brings together the best of the best: dynamic keynote presentations, hands-on technical workshops, expert-led best practice sessions, real-world customer stories, and deep dives into the latest product innovations. Whether you’re looking to sharpen your skills, explore new solutions, or simply stay ahead of where commerce is headed, Unlock 2026 delivers the perfect mix of learning, discovery, and connection.

Now, last year at Unlock, we talked about preparing for the AI wave. That wave is officially here, and it’s reshaping how product experiences are created, managed, and delivered. 

That’s why we’re exploring what comes next: how to modernize your architecture, centralize product data, and evolve your tech stack to support AI-powered commerce at scale. You’ll learn what an AI-ready architecture really looks like, why systems of record are moving back to the center, and how to build a foundation for the next generation of intelligent commerce.

In short, this is where strategy meets reality.

Still on the fence? Here are six reasons Unlock 2026 belongs on your calendar.

6 Reasons to Attend Unlock 2026

1. Gain Insights into What’s New in Akeneo & What’s Coming Next

One thing Unlock can always promise is providing a clear, practical understanding of how Akeneo Product Cloud is evolving. This year, we’ll be featuring a keynote on Akeneo’s 2026 Spring Release with Virginie Blot, Akeneo’s Principal Product Marketing Manager, and a forward-looking roadmap keynote with Andy Tyra, Akeneo’s Chief Product Officer that offers a sneak peek into what Akeneo has planned for the rest of 2026.

Why does this matter? Because product teams, IT leaders, marketers, and eCommerce teams all need to make informed decisions, about architecture, resourcing, integrations, and long-term investments. Seeing new capabilities in action helps you identify opportunities to streamline workflows, reduce manual effort, and deliver richer product experiences faster. Understanding what’s coming next allows you to plan with confidence, align internal teams, and future-proof your commerce strategy.

In short, this is your chance to see how Akeneo’s product vision aligns with your own goals, and to provide you with a roadmap that helps you turn strategy into action.

Check out our product keynote from Unlock 2025!

2. Turn AI Innovation Into Practical, Scalable Execution

AI promises a lot, but turning that promise into consistent, repeatable value is where many organizations struggle.

At Unlock 2026, hands-on labs and workshops are designed to help you move beyond experimentation and into execution. You’ll see how to operationalize AI across product data enrichment, content consistency, and product syndication.

By learning how to automate enrichment, standardize tone of voice, and scale product storytelling across markets and channels, you free up time for higher-value work without sacrificing quality or brand consistency. You’ll leave with practical approaches you can apply immediately to maximize your Akeneo investment and accelerate ROI.

Want to learn how to collaborate between Akeneo PIM, Akeneo DAM, and Akeneo Activation to harmonize product data and assets into engaging experiences tailored for any market? Curious how Akeneo’s Digital Showroom eliminates the friction of outdated collateral? Interested in eliminating repetitive tasks, enforcing data governance, and improving data quality with Akeneo’s Triggered Rules? Our Unlock Labs answer all of these questions and more!

Check out one of our labs from Unlock 2025!

3. Understand How and Why AI-Powered Commerce Demands a Great Restack

With the implementation of AI in shopping experiences across the board, commerce is being fundamentally re-architected.

Expert keynotes at Unlock this year will unpack how AI is reshaping the entire customer journey, from discovery and search to conversion and loyalty. As traditional models are challenged, the role of product data, systems of record, and cross-functional collaboration becomes even more critical.

Why does this matter? Because the decisions you make now about technology, data ownership, and organizational structure will define your ability to compete in an AI-powered future. These keynotes will help you connect high-level trends to real-world implications, giving you the insight needed to modernize your tech stack, align teams, and build a more agile commerce operation.

Check out the Akeneo Opening Keynote from Unlock 2025!

4. Learn What Real-Life AI Success Really Looks Like From Your Peers

AI journeys aren’t linear, and they’re never one-size-fits-all.

Customer panels at Unlock 2026 offer something especially valuable: honest insight from organizations at different stages of AI maturity. From early adopters to innovation leaders, you’ll hear how peers from organizations like Costa Farms, Rainbow Shops, Steelcase, and Hunter Industries are overcoming challenges, proving value, and scaling AI in ways that improve product experiences and business performance.

You’ll gain clarity on what’s achievable, what pitfalls to avoid, and how to measure success, making it easier to build internal alignment and set realistic expectations. The result? Smarter decisions, faster progress, and more confidence in your own AI roadmap.

Check out our PX Champion keynote from Unlock 2025!

5. Expand Your Akeneo Partnership at the Partner Summit

Success in today’s commerce ecosystem isn’t built alone, especially when AI, data, and integration strategies are evolving rapidly. That’s why Unlock 2026 begins a day early with an exclusive Partner Summit on Monday, April 13, a dedicated event designed just for Akeneo partners.

The Partner Summit puts partners front and center of Akeneo’s vision, strategy, and growth plans. You’ll gain early insights into the product direction, innovation priorities, and roadmap thinking that will shape the year ahead. Understanding these strategic threads early gives your business a real edge when advising customers, building solutions, or planning go-to-market activities.

Throughout the day, you’ll experience a blend of inspiring keynotes, strategic discussions, and focused networking, all structured to help you:

• Connect directly with Akeneo leadership and align on where the product and partnership ecosystem are headed.
• Exchange ideas with peers facing similar challenges and opportunities.
• Celebrate partner excellence at the annual Partner Awards, highlighting the leaders driving innovation and success.
• Access practical breakout sessions and workshops that equip you with tools and insights to enhance your offerings and accelerate customer outcomes.

You can learn more and register for the Partner Summit here!

6. Build Meaningful Connections That Last Beyond the Event

Some of the most valuable takeaways from Unlock don’t come from slides. They come from the people you meet.

Unlock is designed to foster meaningful connections across the entire PX community, from hallway conversations and networking breaks to shared learnings in sessions. 

And when the formal agenda wraps, the opportunity to connect continues at the Unlock After Party, taking place on April 14 from 7:30–10:30pm at Punchbowl Social in Chicago’s vibrant West Loop. The venue blends entertainment and dining with activities like bowling, arcade games, and private karaoke rooms, creating a relaxed and fun environment where conversations flow naturally and connections stick.

Stepping out of the conference room and into a shared experience helps you return to work refreshed, and with a rolodex of allies who can support you as commerce continues to evolve (plus, karaoke is simply just fun!).

Best of all, every Unlock ticket includes one free pass to the party, so you can come ready to learn, connect, celebrate, and sing without missing a beat.

Learn more about Akeneo’s annual Unlock party!

Ready to Unlock What’s Next?

Unlock is the time every year where the Product Experience community comes together to learn, connect, and shape what’s next in the new age of commerce. From strategic insights into AI-powered commerce to hands-on learning, real-world customer stories, and unforgettable community moments, Unlock is designed to leave you inspired, informed, and ready to take action.

Now’s the time to secure your spot. Early Bird tickets are available until February 28, and all paid tickets will be entered into a raffle, with the winner announced live at the event to take home a PlayStation 5 console!

Don’t miss your chance to be part of this year’s experience. Register now and join us in Chicago for Unlock 2026. 

Can’t make it to Chicago? No worries – join us for Unlock Digital the following week, April 21-22.